{
    "run_name": "multiband-melgan-rwd",
    "run_description": "multiband melgan with random window discriminator from https://arxiv.org/pdf/1909.11646.pdf",

    // AUDIO PARAMETERS
    "audio":{
        // stft parameters
        "num_freq": 513,         // number of stft frequency levels. Size of the linear spectogram frame.
        "win_length": 1024,      // stft window length in ms.
        "hop_length": 256,       // stft window hop-lengh in ms.
        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.

        // Audio processing parameters
        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.

        // Silence trimming
        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.

        // Griffin-Lim
        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.

        // MelSpectrogram parameters
        "num_mels": 80,         // size of the mel spec frame.
        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!

        // Normalization parameters
        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
        "min_level_db": -100,   // lower bound for normalization
        "symmetric_norm": true, // move normalization to range [-1, 1]
        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
        "clip_norm": true,      // clip normalized values into the range.
        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
    },

    // DISTRIBUTED TRAINING
    // "distributed":{
    //     "backend": "nccl",
    //     "url": "tcp:\/\/localhost:54321"
    // },

    // MODEL PARAMETERS
    "use_pqmf": true,

    // LOSS PARAMETERS
    "use_stft_loss": true,
    "use_subband_stft_loss": true,
    "use_mse_gan_loss": true,
    "use_hinge_gan_loss": false,
    "use_feat_match_loss": false,  // use only with melgan discriminators

    // loss weights
    "stft_loss_weight": 0.5,
    "subband_stft_loss_weight": 0.5,
    "mse_G_loss_weight": 2.5,
    "hinge_G_loss_weight": 2.5,
    "feat_match_loss_weight": 25,

    // multiscale stft loss parameters
    "stft_loss_params": {
        "n_ffts": [1024, 2048, 512],
        "hop_lengths": [120, 240, 50],
        "win_lengths": [600, 1200, 240]
    },

    // subband multiscale stft loss parameters
    "subband_stft_loss_params":{
        "n_ffts": [384, 683, 171],
        "hop_lengths": [30, 60, 10],
        "win_lengths": [150, 300, 60]
    },

    "target_loss": "avg_G_loss",  // loss value to pick the best model to save after each epoch

    // DISCRIMINATOR
    "discriminator_model": "random_window_discriminator",
    "discriminator_model_params":{
        "uncond_disc_donwsample_factors": [8, 4],
        "cond_disc_downsample_factors": [[8, 4, 2, 2, 2], [8, 4, 2, 2], [8, 4, 2], [8, 4], [4, 2, 2]],
        "cond_disc_out_channels": [[128, 128, 256, 256], [128, 256, 256], [128, 256], [256], [128, 256]],
        "window_sizes": [512, 1024, 2048, 4096, 8192]
    },
    "steps_to_start_discriminator": 200000,      // steps required to start GAN trainining.1

    // GENERATOR
    "generator_model": "multiband_melgan_generator",
    "generator_model_params": {
        "upsample_factors":[8, 4, 2],
        "num_res_blocks": 4
    },

    // DATASET
    "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/",
    "seq_len": 16384,
    "pad_short": 2000,
    "conv_pad": 0,
    "use_noise_augment": false,
    "use_cache": true,

    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.

    // TRAINING
    "batch_size": 64,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.

    // VALIDATION
    "run_eval": true,
    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.

    // OPTIMIZER
    "noam_schedule": false,        // use noam warmup and lr schedule.
    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
    "warmup_steps_disc": 4000,
    "epochs": 10000,                // total number of epochs to train.
    "wd": 0.0,                // Weight decay weight.
    "gen_clip_grad": -1,      // Generator gradient clipping threshold. Apply gradient clipping if > 0
    "disc_clip_grad": -1,     // Discriminator gradient clipping threshold.
    "lr_scheduler_gen": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
    "lr_scheduler_gen_params": {
        "gamma": 0.5,
        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
    },
    "lr_scheduler_disc": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
    "lr_scheduler_disc_params": {
        "gamma": 0.5,
        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
    },
    "lr_gen": 1e-4,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
    "lr_disc": 1e-4,

    // TENSORBOARD and LOGGING
    "print_step": 25,       // Number of steps to log traning on console.
    "print_eval": false,     // If True, it prints loss values for each step in eval run.
    "save_step": 25000,      // Number of training steps expected to plot training stats on TB and save model checkpoints.
    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.

    // DATA LOADING
    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
    "eval_split_size": 10,

    // PATHS
    "output_path": "/home/erogol/Models/LJSpeech/"
}